356 citations as recorded by crossref.

1. Streamflow droughts aggravated by human activities despite management A. Van Loon et al. 10.1088/1748-9326/ac5def
2. FOCA: a new quality-controlled database of floods and catchment descriptors in Italy P. Claps et al. 10.5194/essd-16-1503-2024
3. Bayesian extreme learning machines for hydrological prediction uncertainty J. Quilty et al. 10.1016/j.jhydrol.2023.130138
4. Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale G. Papacharalampous et al. 10.1016/j.gsf.2022.101349
5. Building Cross-Site and Cross-Network collaborations in critical zone science B. Arora et al. 10.1016/j.jhydrol.2023.129248
6. Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain R. Lane et al. 10.5194/hess-23-4011-2019
7. NAC2H: The North American Climate Change and Hydroclimatology Data Set R. Arsenault et al. 10.1029/2020WR027097
8. The Abuse of Popular Performance Metrics in Hydrologic Modeling M. Clark et al. 10.1029/2020WR029001
9. Leveraging ensemble meteorological forcing data to improve parameter estimation of hydrologic models H. Liu et al. 10.1002/hyp.14410
10. Diel streamflow cycles suggest more sensitive snowmelt-driven streamflow to climate change than land surface modeling does S. Krogh et al. 10.5194/hess-26-3393-2022
11. Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty 10.1016/j.jhydrol.2023.130498
12. The Impact of Meteorological Forcing Uncertainty on Hydrological Modeling: A Global Analysis of Cryosphere Basins G. Tang et al. 10.1029/2022WR033767
13. Deep Learning for Streamflow Regionalization for Ungauged Basins: Application of Long-Short-Term-Memory Cells in Semiarid Regions F. Nogueira Filho et al. 10.3390/w14091318
14. FlowDyn: A daily streamflow prediction pipeline for dynamical deep neural network applications S. Sadeghi Tabas et al. 10.1016/j.envsoft.2023.105854
15. A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. 10.5194/hess-28-2871-2024
16. Catchment response to climatic variability: implications for root zone storage and streamflow predictions N. Tempel et al. 10.5194/hess-28-4577-2024
17. A hydrologic signature approach to analysing wildfire impacts on overland flow L. Bolotin & H. McMillan 10.1002/hyp.15215
18. Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration Y. Khoshkalam et al. 10.1016/j.jhydrol.2023.129682
19. From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling W. Tsai et al. 10.1038/s41467-021-26107-z
20. From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale? W. Zhi et al. 10.1021/acs.est.0c06783
21. Design flood estimation using extreme Gradient Boosting-based on Bayesian optimization D. Jarajapu et al. 10.1016/j.jhydrol.2022.128341
22. airGRteaching: an open-source tool for teaching hydrological modeling with R O. Delaigue et al. 10.5194/hess-27-3293-2023
23. On understanding mountainous carbonate basins of the Mediterranean using parsimonious modeling solutions S. Azimi et al. 10.5194/hess-27-4485-2023
24. A decomposition approach to evaluating the local performance of global streamflow reanalysis T. Zhao et al. 10.5194/hess-28-3597-2024
25. Improved Regionalization of the CN Method for Extreme Events at Ungauged Sites across the US T. Neelam et al. 10.1061/JHYEFF.HEENG-6180
26. A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) X. Chen et al. 10.5194/essd-15-4463-2023
27. Can We Use the Water Budget to Infer Upland Catchment Behavior? The Role of Data Set Error Estimation and Interbasin Groundwater Flow B. Gordon et al. 10.1029/2021WR030966
28. The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset C. Alvarez-Garreton et al. 10.5194/hess-22-5817-2018
29. To What Extent Are Changes in Flood Magnitude Related to Changes in Precipitation Extremes? H. Do et al. 10.1029/2020GL088684
30. Impact of spatial distribution information of rainfall in runoff simulation using deep learning method Y. Wang & H. Karimi 10.5194/hess-26-2387-2022
31. A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments W. Knoben et al. 10.1029/2019WR025975
32. Evaluating stochastic rainfall models for hydrological modelling T. Nguyen et al. 10.1016/j.jhydrol.2023.130381
33. rabpro: global watershed boundaries, river elevation profiles, and catchment statistics J. Schwenk et al. 10.21105/joss.04237
34. CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain G. Coxon et al. 10.5194/essd-12-2459-2020
35. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al. 10.5194/essd-13-4529-2021
36. Introductory overview: Recommendations for approaching scientific visualization with large environmental datasets C. Kelleher & A. Braswell 10.1016/j.envsoft.2021.105113
37. A Hydrologic Functional Approach for Improving Large‐Sample Hydrology Performance in Poorly Gauged Regions J. Janssen & A. Ameli 10.1029/2021WR030263
38. Gauging ungauged catchments – Active learning for the timing of point discharge observations in combination with continuous water level measurements S. Pool & J. Seibert 10.1016/j.jhydrol.2021.126448
39. Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data K. Tayal et al. 10.1088/1748-9326/ad6fb7
40. Catchment characterization: Current descriptors, knowledge gaps and future opportunities L. Tarasova et al. 10.1016/j.earscirev.2024.104739
41. A dataset of lake-catchment characteristics for the Tibetan Plateau J. Liu et al. 10.5194/essd-14-3791-2022
42. The Treatment of Uncertainty in Hydrometric Observations: A Probabilistic Description of Streamflow Records D. de Oliveira & J. Vrugt 10.1029/2022WR032263
43. Generalized Relationship Linking Water Balance and Vegetation Productivity across Site-to-Regional Scales G. Abeshu et al. 10.1061/JHYEFF.HEENG-6163
44. Location, location, location – Considering relative catchment location to understand subsurface losses M. Kiraz-Safari et al. 10.1016/j.jhydrol.2024.132328
45. Time to Update the Split‐Sample Approach in Hydrological Model Calibration H. Shen et al. 10.1029/2021WR031523
46. A differentiable, physics-based hydrological model and its evaluation for data-limited basins W. Ouyang et al. 10.1016/j.jhydrol.2024.132471
47. Global patterns in vegetation accessible subsurface water storage emerge from spatially varying importance of individual drivers F. van Oorschot et al. 10.1088/1748-9326/ad8805
48. Advancing traditional strategies for testing hydrological model fitness in a changing climate A. Todorović et al. 10.1080/02626667.2022.2104646
49. Train, Inform, Borrow, or Combine? Approaches to Process‐Guided Deep Learning for Groundwater‐Influenced Stream Temperature Prediction J. Barclay et al. 10.1029/2023WR035327
50. Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed T. Xu et al. 10.1029/2021WR030993
51. How is Baseflow Index (BFI) impacted by water resource management practices? J. Bloomfield et al. 10.5194/hess-25-5355-2021
52. Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132269
53. Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model H. Yin et al. 10.1016/j.jhydrol.2021.126378
54. Spatial Delimitation of Small Headwater Catchments and Their Classification in Terms of Runoff Risks P. Kavka 10.3390/w13233458
55. Time Series Features for Supporting Hydrometeorological Explorations and Predictions in Ungauged Locations Using Large Datasets G. Papacharalampous & H. Tyralis 10.3390/w14101657
56. MacroSheds: A synthesis of long‐term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies M. Vlah et al. 10.1002/lol2.10325
57. Comprehensive assessment of baseflow responses to long-term meteorological droughts across the United States S. Lee & H. Ajami 10.1016/j.jhydrol.2023.130256
58. Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models R. Arsenault et al. 10.5194/hess-27-139-2023
59. Benchmarking high-resolution hydrologic model performance of long-term retrospective streamflow simulations in the contiguous United States E. Towler et al. 10.5194/hess-27-1809-2023
60. Future streamflow regime changes in the United States: assessment using functional classification M. Brunner et al. 10.5194/hess-24-3951-2020
61. Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E) J. Mai et al. 10.1061/(ASCE)HE.1943-5584.0002097
62. Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al. 10.1016/j.jhydrol.2022.127736
63. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132003
64. The persistence of snow on the ground affects the shape of streamflow hydrographs over space and time: a continental-scale analysis E. Le et al. 10.3389/fenvs.2023.1207508
65. Mapping (dis)agreement in hydrologic projections L. Melsen et al. 10.5194/hess-22-1775-2018
66. Uncertainties in measuring and estimating water‐budget components: Current state of the science S. Levin et al. 10.1002/wat2.1646
67. Dual-Stage Attention-Based LSTM Network for Multiple Time Steps Flood Forecasting F. Wang et al. 10.5194/piahs-386-141-2024
68. Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis 10.5194/adgeo-45-201-2018
69. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
70. Beyond Counting Zeroes: Using Entropy-Based Hydrologic Signatures and Classification for Streamflow Intermittency Assessment I. Niadas & C. Makropoulos 10.1007/s11269-024-03881-1
71. Adaptive Reservoir Inflow Forecasting Using Variational Mode Decomposition and Long Short-Term Memory H. Hu et al. 10.1109/ACCESS.2021.3107502
72. HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists R. Rigon et al. 10.5194/hess-26-4773-2022
73. Streamflow-based evaluation of climate model sub-selection methods J. Kiesel et al. 10.1007/s10584-020-02854-8
74. Many Commonly Used Rainfall‐Runoff Models Lack Long, Slow Dynamics: Implications for Runoff Projections K. Fowler et al. 10.1029/2019WR025286
75. Regionalization for Ungauged Catchments — Lessons Learned From a Comparative Large‐Sample Study S. Pool et al. 10.1029/2021WR030437
76. Runoff for Russia (RFR v1.0): The Large-Sample Dataset of Simulated Runoff and Its Characteristics G. Ayzel 10.3390/data8020031
77. Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets F. Hasan et al. 10.3390/w16131904
78. Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US G. Konapala et al. 10.1088/1748-9326/aba927
79. Correspondence Between Model Structures and Hydrological Signatures: A Large‐Sample Case Study Using 508 Brazilian Catchments P. David et al. 10.1029/2021WR030619
80. QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al. 10.5194/essd-14-3715-2022
81. Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff D. Althoff & G. Destouni 10.1016/j.oneear.2023.08.002
82. Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. 10.3390/w16060896
83. CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil V. Chagas et al. 10.5194/essd-12-2075-2020
84. Impacts of agriculture and snow dynamics on catchment water balance in the U.S. and Great Britain M. Zaerpour et al. 10.1038/s43247-024-01891-w
85. Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. 10.1002/wat2.1520
86. Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al. 10.5194/hess-25-5517-2021
87. Evaluation of distributed process-based hydrologic model performance using only a priori information to define model inputs S. Bhanja et al. 10.1016/j.jhydrol.2023.129176
88. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
89. Generating interpretable rainfall-runoff models automatically from data T. Dantzer & B. Kerkez 10.1016/j.advwatres.2024.104796
90. Interactions between snow cover and evaporation lead to higher sensitivity of streamflow to temperature A. Meira Neto et al. 10.1038/s43247-020-00056-9
91. Modeling hydraulic heads with impulse response functions in different environmental settings of the Baltic countries M. Jemeļjanova et al. 10.1016/j.ejrh.2023.101416
92. Toward interpretable LSTM-based modeling of hydrological systems L. De la Fuente et al. 10.5194/hess-28-945-2024
93. Flood Frequency Modeling and Prediction of Beki and Pagladia Rivers Using Deep Learning Approach G. Devi et al. 10.1007/s11063-022-10773-1
94. Where should hydrology go? An early-career perspective on the next IAHS Scientific Decade: 2023–2032 T. van Hateren et al. 10.1080/02626667.2023.2170754
95. Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction T. Xie et al. 10.3390/w16010069
96. A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies Z. Yin et al. 10.5194/essd-16-1559-2024
97. On the challenges of global entity-aware deep learning models for groundwater level prediction B. Heudorfer et al. 10.5194/hess-28-525-2024
98. Higher-order internal modes of variability imprinted in year-to-year California streamflow changes S. Duan et al. 10.1038/s43247-024-01594-2
99. Comparison of eight filter-based feature selection methods for monthly streamflow forecasting – Three case studies on CAMELS data sets K. Ren et al. 10.1016/j.jhydrol.2020.124897
100. Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network C. Frank et al. 10.3389/frwa.2023.1126310
101. A benchmark dataset for machine learning in ecotoxicology C. Schür et al. 10.1038/s41597-023-02612-2
102. Exploring Hydrologic Model Process Connectivity at the Continental Scale Through an Information Theory Approach G. Konapala et al. 10.1029/2020WR027340
103. Using R in hydrology: a review of recent developments and future directions L. Slater et al. 10.5194/hess-23-2939-2019
104. Use of streamflow indices to identify the catchment drivers of hydrographs J. Mathai & P. Mujumdar 10.5194/hess-26-2019-2022
105. A Novel Approach for High-Performance Estimation of SPI Data in Drought Prediction L. Latifoğlu & M. Özger 10.3390/su151914046
106. Climate and rivers G. McGregor 10.1002/rra.3508
107. Streamflow regime of a lake‐stream system based on long‐term data from a high‐density hydrometric network D. Hudson et al. 10.1002/hyp.14396
108. Runoff Prediction Based on Deep Residual Shrinkage Long Short-term Memory Network Z. Guan et al. 10.1088/1742-6596/2400/1/012016
109. Can transfer learning improve hydrological predictions in the alpine regions? Y. Yao et al. 10.1016/j.jhydrol.2023.130038
110. Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems E. Uncuoglu et al. 10.1016/j.asoc.2022.109623
111. Catchment processes can amplify the effect of increasing rainfall variability M. Müller et al. 10.1088/1748-9326/ac153e
112. Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction C. Deng et al. 10.1016/j.jenvman.2024.121299
113. Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks A. Sun et al. 10.1029/2021WR030394
114. On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al. 10.5194/hess-28-5011-2024
115. Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network M. Gauch et al. 10.5194/hess-25-2045-2021
116. Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs Y. Shen et al. 10.5194/essd-15-2781-2023
117. Dissolving the mystery of subsurface controls on snowmelt–discharge dynamics in karst mountain watersheds using hydrologic timeseries D. Thurber et al. 10.1002/hyp.15170
118. Post‐Processing the National Water Model with Long Short‐Term Memory Networks for Streamflow Predictions and Model Diagnostics J. Frame et al. 10.1111/1752-1688.12964
119. Solute export patterns across the contiguous USA D. Kincaid et al. 10.1002/hyp.15197
120. An Efficient Estimation of Time‐Varying Parameters of Dynamic Models by Combining Offline Batch Optimization and Online Data Assimilation Y. Sawada 10.1029/2021MS002882
121. Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins Y. Sawada et al. 10.3178/hrl.16.73
122. Theoretical and empirical evidence against the Budyko catchment trajectory conjecture N. Reaver et al. 10.5194/hess-26-1507-2022
123. Hydrologic Sensitivities and ENSO Variability Across Hydrological Regimes in Central Chile (28°–41°S) D. Hernandez et al. 10.1029/2021WR031860
124. Learning landscape features from streamflow with autoencoders A. Bassi et al. 10.5194/hess-28-4971-2024
125. Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning S. Jiang et al. 10.1029/2020GL088229
126. A unified framework of water balance models for monthly, annual, and mean annual timescales X. Zhang et al. 10.1016/j.jhydrol.2020.125186
127. Uncertainty estimation with deep learning for rainfall–runoff modeling D. Klotz et al. 10.5194/hess-26-1673-2022
128. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. 10.3389/frwa.2023.1166124
129. A Novel Strategy for Automatic Selection of Cross‐Basin Data to Improve Local Machine Learning‐Based Runoff Models C. Nai et al. 10.1029/2023WR035051
130. Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization S. Ragettli et al. 10.1016/j.jhydrol.2017.10.031
131. A multiple hydrograph separation technique for identifying hydrological model structures and an interpretation of dominant process controls on flow duration curves C. Leong & Y. Yokoo 10.1002/hyp.14569
132. The proper care and feeding of CAMELS: How limited training data affects streamflow prediction M. Gauch et al. 10.1016/j.envsoft.2020.104926
133. A hybrid Budyko-type regression framework for estimating baseflow from climate and catchment attributes S. Chen & X. Ruan 10.1016/j.jhydrol.2023.129118
134. Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. 10.3390/w11102126
135. A new criteria for determining the best decomposition level and filter for wavelet-based data-driven forecasting frameworks- validating using three case studies on the CAMELS dataset M. Mazarei Behbahani & A. Mazarei 10.1007/s00477-023-02531-z
136. Direct vegetation response to recent CO2 rise shows limited effect on global streamflow H. Wei et al. 10.1038/s41467-024-53879-x
137. Meta-LSTM in hydrology: Advancing runoff predictions through model-agnostic meta-learning K. Cai et al. 10.1016/j.jhydrol.2024.131521
138. Vegetation patterns governing the competitive relationship between runoff and evapotranspiration using a novel water balance model at a semi-arid watershed Q. Fang et al. 10.1016/j.envres.2022.113976
139. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. 10.1016/j.jhydrol.2023.129160
140. Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow H. Tyralis & G. Papacharalampous 10.5194/adgeo-45-147-2018
141. National-scale geodatabase of catchment characteristics in the Philippines for river management applications R. Boothroyd et al. 10.1371/journal.pone.0281933
142. Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions K. Ma et al. 10.1029/2020WR028600
143. Diagnostic Evaluation of Large‐Domain Hydrologic Models Calibrated Across the Contiguous United States O. Rakovec et al. 10.1029/2019JD030767
144. TOSSH: A Toolbox for Streamflow Signatures in Hydrology S. Gnann et al. 10.1016/j.envsoft.2021.104983
145. Catchment natural driving factors and prediction of baseflow index for Continental United States based on Random Forest technique S. Huang et al. 10.1007/s00477-021-02057-2
146. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships K. Xie et al. 10.1016/j.jhydrol.2021.127043
147. Quantifying the Geomorphic Effect of Floods Using Satellite Observations of River Mobility A. Leenman et al. 10.1029/2023GL103875
148. Seasonality and Drivers of Low Flows Across Europe and the United States M. Floriancic et al. 10.1029/2019WR026928
149. Spatially Distributed Overstory and Understory Leaf Area Index Estimated from Forest Inventory Data S. Goeking & D. Tarboton 10.3390/w14152414
150. Runoff predictions in new-gauged basins using two transformer-based models H. Yin et al. 10.1016/j.jhydrol.2023.129684
151. What control the spatial patterns and predictions of runoff response over the contiguous USA? S. Jiang et al. 10.1007/s11442-024-2249-4
152. Technical note: Do different projections matter for the Budyko framework? R. Nijzink & S. Schymanski 10.5194/hess-26-4575-2022
153. Variational Bayesian dropout with a Gaussian prior for recurrent neural networks application in rainfall–runoff modeling S. Sadeghi Tabas & S. Samadi 10.1088/1748-9326/ac7247
154. The importance of source data in river network connectivity modeling: A review C. Brinkerhoff 10.1002/lno.12706
155. What is the hydrologically effective area of a catchment? Y. Liu et al. 10.1088/1748-9326/aba7e5
156. The role of previously glaciated landscapes in spatiotemporal variability of streamflow in snow-dominated watersheds: British Columbia, Canada X. Chen et al. 10.1016/j.ejrh.2023.101478
157. Horton Index: Conceptual Framework for Exploring Multi‐Scale Links Between Catchment Water Balance and Vegetation Dynamics G. Abeshu & H. Li 10.1029/2020WR029343
158. Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions D. Klotz et al. 10.5194/hess-28-3665-2024
159. Enhancing Predictions in Ungauged Basins Using Machine Learning to Its Full Potential T. Fadziso 10.18034/ajase.v8i1.10
160. Technical note: Complexity–uncertainty curve (c-u-curve) – a method to analyse, classify and compare dynamical systems U. Ehret & P. Dey 10.5194/hess-27-2591-2023
161. Variable Streamflow Response to Forest Disturbance in the Western US: A Large‐Sample Hydrology Approach S. Goeking & D. Tarboton 10.1029/2021WR031575
162. AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods A. Abbas et al. 10.5194/gmd-15-3021-2022
163. Machine‐Learning Reveals Equifinality in Drivers of Stream DOC Concentration at Continental Scales K. Underwood et al. 10.1029/2021WR030551
164. Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta 10.1016/j.ejrh.2023.101607
165. Climate Controls on River Chemistry L. Li et al. 10.1029/2021EF002603
166. Hydrological Drought Simulations: How Climate and Model Structure Control Parameter Sensitivity L. Melsen & B. Guse 10.1029/2019WR025230
167. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. 10.1016/j.jhydrol.2023.130107
168. The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU: A case study for six basins from the CAMELS dataset G. Ayzel & M. Heistermann 10.1016/j.cageo.2021.104708
169. Modeling streamflow variability at the regional scale: (1) perceptual model development through signature analysis F. Fenicia & J. McDonnell 10.1016/j.jhydrol.2021.127287
170. Crossing the rural–urban boundary in hydrological modelling: How do conceptual rainfall–runoff models handle the specificities of urbanized catchments? M. Saadi et al. 10.1002/hyp.13808
171. A step toward global-scale applicability and transferability of flow duration curve studies: A flow duration curve review (2000–2020) C. Leong & Y. Yokoo 10.1016/j.jhydrol.2021.126984
172. How Do Climate and Catchment Attributes Influence Flood Generating Processes? A Large‐Sample Study for 671 Catchments Across the Contiguous USA L. Stein et al. 10.1029/2020WR028300
173. Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA T. Barnhart et al. 10.1002/rra.4028
174. On the use of distribution-adaptive likelihood functions: Generalized and universal likelihood functions, scoring rules and multi-criteria ranking J. Vrugt et al. 10.1016/j.jhydrol.2022.128542
175. Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. 10.1029/2021WR031751
176. What controls hydrology? An assessment across the contiguous United States through an interpretable machine learning approach K. Li & S. Razavi 10.1016/j.jhydrol.2024.131835
177. A Multi-Task Learning Based Runoff Forecasting Model for Multi-Scale Chaotic Hydrological Time Series H. Zuo et al. 10.1007/s11269-023-03681-z
178. A Global-Scale Investigation of Stochastic Similarities in Marginal Distribution and Dependence Structure of Key Hydrological-Cycle Processes P. Dimitriadis et al. 10.3390/hydrology8020059
179. Regionalization in a Global Hydrologic Deep Learning Model: From Physical Descriptors to Random Vectors X. Li et al. 10.1029/2021WR031794
180. Development of the consider cubature Kalman filter for state estimation of hydrological models with parameter uncertainty Y. Sun et al. 10.1016/j.jhydrol.2023.130080
181. Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets F. Kratzert et al. 10.5194/hess-23-5089-2019
182. Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management L. Slater et al. 10.5194/hess-25-3897-2021
183. Hydrological post-processing for predicting extreme quantiles H. Tyralis & G. Papacharalampous 10.1016/j.jhydrol.2023.129082
184. Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios M. Buechel et al. 10.1038/s43247-021-00334-0
185. Spatial Dependence of Floods Shaped by Spatiotemporal Variations in Meteorological and Land‐Surface Processes M. Brunner et al. 10.1029/2020GL088000
186. Time‐Variability of Flow Recession Dynamics: Application of Machine Learning and Learning From the Machine M. Kim et al. 10.1029/2022WR032690
187. CCAM: China Catchment Attributes and Meteorology dataset Z. Hao et al. 10.5194/essd-13-5591-2021
188. Malá zdrojová povodí – jejich prostorové vymezení a klasifikace z hlediska rizika ohrožení rychlým odtokem P. Kavka et al. 10.46555/VTEI.2022.11.001
189. Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting J. Qu et al. 10.1007/s11269-021-02770-1
190. LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al. 10.1007/s00506-021-00769-x
191. Pitfalls and a feasible solution for using KGE as an informal likelihood function in MCMC methods: DREAM(ZS) as an example Y. Liu et al. 10.5194/hess-26-5341-2022
192. Investigating the potential of EMA-embedded feature selection method for ESVR and LSTM to enhance the robustness of monthly streamflow forecasting from local meteorological information L. Xu et al. 10.1016/j.jhydrol.2024.131230
193. Upland Hillslope Groundwater Subsidy Affects Low‐Flow Storage–Discharge Relationship H. Li & A. Ameli 10.1029/2022WR034155
194. Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. 10.1016/j.jhydrol.2024.131598
195. Toward an improved estimation of flood frequency statistics from simulated flows L. Hu et al. 10.1111/jfr3.12891
196. Hydrological signatures describing the translation of climate seasonality into streamflow seasonality S. Gnann et al. 10.5194/hess-24-561-2020
197. Retracted: Spatiotemporal convolutional long short-term memory for regional streamflow predictions A. Mohammed & G. Corzo 10.1016/j.jenvman.2023.119585
198. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al. 10.5194/hess-28-2107-2024
199. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al. 10.5194/hess-26-5085-2022
200. Increasing importance of temperature as a contributor to the spatial extent of streamflow drought M. Brunner et al. 10.1088/1748-9326/abd2f0
201. The Bellinge data set: open data and models for community-wide urban drainage systems research A. Nedergaard Pedersen et al. 10.5194/essd-13-4779-2021
202. Improving deep learning-based streamflow forecasting under trend varying conditions through evaluation of new wavelet preprocessing technique M. Behbahani et al. 10.1007/s00477-024-02788-y
203. Disentangling coastal groundwater level dynamics in a global dataset A. Nolte et al. 10.5194/hess-28-1215-2024
204. Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach M. Brunner & E. Gilleland 10.5194/hess-24-3967-2020
205. A two-stage partitioning monthly model and assessment of its performance on runoff modeling C. Deng & W. Wang 10.1016/j.jhydrol.2020.125829
206. Integration of Generative-Adversarial-Network-Based Data Compaction and Spatial Attention Transductive Long Short-Term Memory for Improved Rainfall–Runoff Modeling B. Ghanati & J. Serra-Sagristà 10.3390/rs16203889
207. Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms H. Tyralis et al. 10.3390/rs13030333
208. Classification of watersheds in the conterminous United States using shape-based time-series clustering and Random Forests M. Yang & F. Olivera 10.1016/j.jhydrol.2023.129409
209. Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms H. Tyralis et al. 10.1007/s00521-020-05172-3
210. Deep learning in water protection of resources, environment, and ecology: achievement and challenges X. Fu et al. 10.1007/s11356-024-31963-5
211. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al. 10.2166/hydro.2024.268
212. Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework T. Botterill & H. McMillan 10.1029/2022WR033091
213. Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data S. Gharari et al. 10.1029/2020WR027948
214. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia K. Fowler et al. 10.5194/essd-13-3847-2021
215. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al. 10.5194/hess-26-5493-2022
216. Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis E. Moges et al. 10.3390/w13010028
217. Rainfall-runoff modeling using long short-term memory based step-sequence framework H. Yin et al. 10.1016/j.jhydrol.2022.127901
218. Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment M. Dal Molin et al. 10.5194/hess-24-1319-2020
219. Assimilation of remotely sensed evapotranspiration products for streamflow simulation based on the CAMELS data sets C. Deng et al. 10.1016/j.jhydrol.2023.130574
220. Assessing the performance and robustness of two conceptual rainfall-runoff models on a worldwide sample of watersheds T. Mathevet et al. 10.1016/j.jhydrol.2020.124698
221. Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges N. Addor et al. 10.1080/02626667.2019.1683182
222. Getting your money's worth: Testing the value of data for hydrological model calibration J. Seibert et al. 10.1002/hyp.15094
223. Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning S. Kim et al. 10.1038/s41597-024-03959-w
224. Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks F. Kratzert et al. 10.5194/hess-22-6005-2018
225. Hyper-resolution PCR-GLOBWB: opportunities and challenges from refining model spatial resolution to 1 km over the European continent J. Hoch et al. 10.5194/hess-27-1383-2023
226. Taking full advantage of ‘low-quality’ datasets in watershed modeling and management: From a perspective of parameter calibration Y. Rong et al. 10.1016/j.jenvman.2023.119955
227. A process-driven deep learning hydrological model for daily rainfall-runoff simulation H. Li et al. 10.1016/j.jhydrol.2024.131434
228. Vegetation optimality explains the convergence of catchments on the Budyko curve R. Nijzink & S. Schymanski 10.5194/hess-26-6289-2022
229. Large Ensemble Diagnostic Evaluation of Hydrologic Parameter Uncertainty in the Community Land Model Version 5 (CLM5) H. Yan et al. 10.1029/2022MS003312
230. When good signatures go bad: Applying hydrologic signatures in large sample studies H. McMillan et al. 10.1002/hyp.14987
231. An autoencoder-based snow drought index S. Rasiya Koya et al. 10.1038/s41598-023-47999-5
232. CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland M. Höge et al. 10.5194/essd-15-5755-2023
233. Large-sample hydrology – a few camels or a whole caravan? F. Clerc-Schwarzenbach et al. 10.5194/hess-28-4219-2024
234. Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores W. Knoben et al. 10.5194/hess-23-4323-2019
235. Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al. 10.5194/hess-28-2505-2024
236. Exploring Signature‐Based Model Calibration for Streamflow Prediction in Ungauged Basins M. Dal Molin et al. 10.1029/2022WR031929
237. Control of climate and physiography on runoff response behavior through use of catchment classification and machine learning S. Du et al. 10.1016/j.scitotenv.2023.166422
238. A statistical approach for identifying factors governing streamflow recession behaviour H. Li & A. Ameli 10.1002/hyp.14718
239. An Aridity Index‐Based Formulation of Streamflow Components A. Meira Neto et al. 10.1029/2020WR027123
240. Impacts of El Niño–southern oscillation on global runoff: Characteristic signatures and potential mechanisms Y. You et al. 10.1002/hyp.14367
241. Estimating the Routing Parameter of the Xin’anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning Y. Fang et al. 10.3390/rs14184609
242. Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States H. Cai et al. 10.1016/j.ejrh.2021.100930
243. A Quantitative Hydrological Climate Classification Evaluated With Independent Streamflow Data W. Knoben et al. 10.1029/2018WR022913
244. To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al. 10.5194/hess-28-2705-2024
245. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
246. Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise W. Knoben & D. Spieler 10.5194/hess-26-3299-2022
247. The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata H. Do et al. 10.5194/essd-10-765-2018
248. GRQA: Global River Water Quality Archive H. Virro et al. 10.5194/essd-13-5483-2021
249. Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability L. Trotter et al. 10.5194/gmd-15-6359-2022
250. PISCO_HyM_GR2M: A Model of Monthly Water Balance in Peru (1981–2020) H. Llauca et al. 10.3390/w13081048
251. A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis 10.3389/frwa.2022.961954
252. Time‐Varying Sensitivity Analysis Reveals Relationships Between Watershed Climate and Variations in Annual Parameter Importance in Regions With Strong Interannual Variability R. Basijokaite & C. Kelleher 10.1029/2020WR028544
253. Leveraging gauge networks and strategic discharge measurements to aid the development of continuous streamflow records M. Vlah et al. 10.5194/hess-28-545-2024
254. CABra: a novel large-sample dataset for Brazilian catchments A. Almagro et al. 10.5194/hess-25-3105-2021
255. Decreases in Mean Annual Streamflow and Interannual Streamflow Variability Across Snow‐Affected Catchments Under a Warming Climate Z. Liu et al. 10.1029/2021GL097442
256. Multi‐Task Deep Learning of Daily Streamflow and Water Temperature J. Sadler et al. 10.1029/2021WR030138
257. Assessing the simulation of streamflow with the LSTM model across the continental United States using the MOPEX dataset A. Tounsi et al. 10.1007/s00521-023-08922-1
258. Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma 10.1016/j.jhydrol.2024.130635
259. How Probable Is Widespread Flooding in the United States? M. Brunner et al. 10.1029/2020WR028096
260. Ecosystem adaptation to climate change: the sensitivity of hydrological predictions to time-dynamic model parameters L. Bouaziz et al. 10.5194/hess-26-1295-2022
261. Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges E. Ragno et al. 10.5194/hess-26-1695-2022
262. Why does snowmelt-driven streamflow response to warming vary? A data-driven review and predictive framework B. Gordon et al. 10.1088/1748-9326/ac64b4
263. Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama H. Gholizadeh et al. 10.1016/j.scitotenv.2023.165884
264. Caravan - A global community dataset for large-sample hydrology F. Kratzert et al. 10.1038/s41597-023-01975-w
265. Using hydrological and climatic catchment clusters to explore drivers of catchment behavior F. Jehn et al. 10.5194/hess-24-1081-2020
266. Implications of model selection: a comparison of publicly available, conterminous US-extent hydrologic component estimates S. Saxe et al. 10.5194/hess-25-1529-2021
267. Toward reproducible and interoperable environmental modeling: Integration of HydroShare with server-side methods for exposing large-extent spatial datasets to models Y. Choi et al. 10.1016/j.envsoft.2024.106239
268. Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach A. Alexander & D. Kumar 10.1016/j.advwatres.2024.104842
269. Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States K. Khand & G. Senay 10.1016/j.mlwa.2024.100551
270. Exploring the provenance of information across Canadian hydrometric stations: implications for discharge estimation and uncertainty quantification S. Gharari et al. 10.5194/hess-28-4383-2024
271. Improving the Representation of Subsurface Water Movement in Land Models S. Gharari et al. 10.1175/JHM-D-19-0108.1
272. Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model J. Aerts et al. 10.5194/hess-26-4407-2022
273. Comparison of flood simulation capabilities of a hydrologic model and a machine learning model L. Liu et al. 10.1002/joc.7738
274. CAMELS-Chem: augmenting CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) with atmospheric and stream water chemistry data G. Sterle et al. 10.5194/hess-28-611-2024
275. On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process J. Frame et al. 10.1002/hyp.14847
276. What Role Does Hydrological Science Play in the Age of Machine Learning? G. Nearing et al. 10.1029/2020WR028091
277. Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling S. Razavi 10.1016/j.envsoft.2021.105159
278. Direct and Indirect Drivers of Energy and Nutrient Availability in Freshwater Ecosystems Across Spatial Scales H. Fazekas et al. 10.1029/2022GB007580
279. Profiling and Pairing Catchments and Hydrological Models With Latent Factor Model Y. Yang & T. Chui 10.1029/2022WR033684
280. Continental-scale streamflow modeling of basins with reservoirs: Towards a coherent deep-learning-based strategy W. Ouyang et al. 10.1016/j.jhydrol.2021.126455
281. Soil CO2 Controls Short‐Term Variation but Climate Regulates Long‐Term Mean of Riverine Inorganic Carbon B. Stewart et al. 10.1029/2022GB007351
282. Parameter's Controls of Distributed Catchment Models—How Much Information is in Conventional Catchment Descriptors? R. Merz et al. 10.1029/2019WR026008
283. Varying Importance of Storm Types and Antecedent Conditions for Local and Regional Floods M. Brunner & E. Dougherty 10.1029/2022WR033249
284. A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al. 10.1016/j.jhydrol.2023.129269
285. Assessment of Future Risks of Seasonal Municipal Water Shortages Across North America J. Janssen et al. 10.3389/feart.2021.730631
286. Regional Patterns and Physical Controls of Streamflow Generation Across the Conterminous United States S. Wu et al. 10.1029/2020WR028086
287. Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method H. Cai et al. 10.1016/j.jhydrol.2022.128495
288. Historical and future changes in global flood magnitude – evidence from a model–observation investigation H. Do et al. 10.5194/hess-24-1543-2020
289. Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning F. Kratzert et al. 10.1029/2019WR026065
290. Hydrological characteristics of Australia: national catchment classification and regional relationships J. Jaffrés et al. 10.1016/j.jhydrol.2022.127969
291. On the choice of calibration metrics for “high-flow” estimation using hydrologic models N. Mizukami et al. 10.5194/hess-23-2601-2019
292. Deep learning for water quality W. Zhi et al. 10.1038/s44221-024-00202-z
293. Runoff predictions in ungauged basins using sequence-to-sequence models H. Yin et al. 10.1016/j.jhydrol.2021.126975
294. Hydrologic Model Sensitivity to Temporal Aggregation of Meteorological Forcing Data: A Case Study for the Contiguous United States A. Van Beusekom et al. 10.1175/JHM-D-21-0111.1
295. Examination and comparison of binary metaheuristic wrapper-based input variable selection for local and global climate information-driven one-step monthly streamflow forecasting K. Ren et al. 10.1016/j.jhydrol.2021.126152
296. Canada Source Watershed Polygons (Can-SWaP): A dataset for the protection of Canada’s municipal water supply F. Robinne et al. 10.1038/s41597-023-02732-9
297. Another Step Toward “Big” Catchment Science M. Vlah et al. 10.1002/lob.10590
298. Including Regional Knowledge Improves Baseflow Signature Predictions in Large Sample Hydrology S. Gnann et al. 10.1029/2020WR028354
299. Simultaneous Calibration of Hydrologic Model Structure and Parameters Using a Blended Model R. Chlumsky et al. 10.1029/2020WR029229
300. Characterizing uncertainty in Community Land Model version 5 hydrological applications in the United States H. Yan et al. 10.1038/s41597-023-02049-7
301. Strength and Memory of Precipitation's Control Over Streamflow Across the Conterminous United States E. Moges et al. 10.1029/2021WR030186
302. A parsimonious daily water balance model based on the proportionality hypothesis S. Wu et al. 10.1016/j.jhydrol.2024.132414
303. Soil drainage modulates climate effects to shape seasonal and mean annual water balances across the southeastern United States Z. Wang et al. 10.1002/hyp.15214
304. Transfer performance of gated recurrent unit model for runoff prediction based on the comprehensive spatiotemporal similarity of catchments X. Min et al. 10.1016/j.jenvman.2022.117182
305. A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven 10.1002/wat2.1761
306. In Quest of Calibration Density and Consistency in Hydrologic Modeling: Distributed Parameter Calibration against Streamflow Characteristics Y. Yang et al. 10.1029/2018WR024178
307. Advances in Quantifying Streamflow Variability Across Continental Scales: 1. Identifying Natural and Anthropogenic Controlling Factors in the USA Using a Spatially Explicit Modeling Method R. Alexander et al. 10.1029/2019WR025001
308. Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS H. Tyralis et al. 10.1016/j.jhydrol.2019.123957
309. A Ranking of Hydrological Signatures Based on Their Predictability in Space N. Addor et al. 10.1029/2018WR022606
310. A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al. 10.5194/hess-28-4099-2024
311. The influence of regional hydrometric data incorporation on the accuracy of gridded reconstruction of monthly runoff G. Ayzel et al. 10.1080/02626667.2020.1762886
312. PatagoniaMet: A multi-source hydrometeorological dataset for Western Patagonia R. Aguayo et al. 10.1038/s41597-023-02828-2
313. Streamflow prediction in ungauged catchments through use of catchment classification and deep learning M. He et al. 10.1016/j.jhydrol.2024.131638
314. Comparing three machine learning algorithms with existing methods for natural streamflow estimation S. Mehrvand et al. 10.1080/02626667.2023.2273402
315. Climate change and the opportunity cost of conflict K. Roche et al. 10.1073/pnas.1914829117
316. Combining global precipitation data and machine learning to predict flood peaks in ungauged areas with similar climate Z. Rasheed et al. 10.1016/j.advwatres.2024.104781
317. A water-energy complementary model for monthly runoff simulation Y. Zou et al. 10.1016/j.jhydrol.2024.131624
318. How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset H. Tyralis et al. 10.1016/j.jhydrol.2019.04.070
319. An evaluation of random forest based input variable selection methods for one month ahead streamflow forecasting W. Fang et al. 10.1038/s41598-024-81502-y
320. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. 10.5194/hess-26-3377-2022
321. Earthquake Nowcasting with Deep Learning G. Fox et al. 10.3390/geohazards3020011
322. Quantile-Based Hydrological Modelling H. Tyralis & G. Papacharalampous 10.3390/w13233420
323. Changes in streamflow statistical structure across the United States due to recent climate change A. Gupta et al. 10.1016/j.jhydrol.2023.129474
324. Is There a Baseflow Budyko Curve? S. Gnann et al. 10.1029/2018WR024464
325. Benchmarking data-driven rainfall-runoff modeling across 54 catchments in the Yellow River Basin: Overfitting, calibration length, dry frequency J. Jin et al. 10.1016/j.ejrh.2022.101119
326. EStreams: An integrated dataset and catalogue of streamflow, hydro-climatic and landscape variables for Europe T. do Nascimento et al. 10.1038/s41597-024-03706-1
327. Factors influencing calibration of a semi-distributed mixed runoff hydrological model: A study on nine small mountain catchments in China L. Wen et al. 10.1016/j.ejrh.2023.101418
328. Expectile-based hydrological modelling for uncertainty estimation: Life after mean H. Tyralis et al. 10.1016/j.jhydrol.2022.128986
329. A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling F. Kratzert et al. 10.5194/hess-25-2685-2021
330. Using Physics-Encoded GeoAI to Improve the Physical Realism of Deep Learning′s Rainfall-Runoff Responses under Climate Change H. Li et al. 10.1016/j.jag.2024.104101
331. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al. 10.1016/j.jhydrol.2022.127781
332. HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool E. Moges et al. 10.3389/feart.2022.884766
333. Investigating the effects of local weather, streamflow lag, and global climate information on 1-month-ahead streamflow forecasting by using XGBoost and SHAP: two case studies involving the contiguous USA J. Liu et al. 10.1007/s11600-022-00928-y
334. Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti R. Bathelemy et al. 10.5194/essd-16-2073-2024
335. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al. 10.5194/hess-28-4187-2024
336. Inference of Parameters for a Global Hydrological Model: Identifiability and Predictive Uncertainties of Climate‐Based Parameters T. Yoshida et al. 10.1029/2021WR030660
337. A comprehensive, multisource database for hydrometeorological modeling of 14,425 North American watersheds R. Arsenault et al. 10.1038/s41597-020-00583-2
338. Drivers of Dissolved Organic Carbon Mobilization From Forested Headwater Catchments: A Multi Scaled Approach T. Adler et al. 10.3389/frwa.2021.578608
339. Mechanism Analysis and Demonstration of Effective Information Extraction in the System Differential Response Inversion Estimation Method Y. Chen et al. 10.3390/w15224016
340. Can model structure families be inferred from model output? J. Remmers et al. 10.1016/j.envsoft.2020.104817
341. Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling K. Li et al. 10.5194/hess-25-4947-2021
342. LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason & B. Nijssen 10.5194/essd-16-2741-2024
343. A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes J. Quilty & J. Adamowski 10.1016/j.envsoft.2020.104718
344. Findings from a National Survey of Canadian perspectives on predicting river channel migration and river bank erosion C. Kupferschmidt & A. Binns 10.1002/rra.4336
345. Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence S. Rasiya Koya & T. Roy 10.1016/j.jhydrol.2024.131301
346. Data‐Driven Worldwide Quantification of Large‐Scale Hydroclimatic Covariation Patterns and Comparison With Reanalysis and Earth System Modeling N. Ghajarnia et al. 10.1029/2020WR029377
347. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. 10.5194/hess-26-3377-2022
348. Advancing streamflow prediction in data-scarce regions through vegetation-constrained distributed hybrid ecohydrological models L. Zhong et al. 10.1016/j.jhydrol.2024.132165
349. Setting expectations for hydrologic model performance with an ensemble of simple benchmarks W. Knoben 10.1002/hyp.15288
350. When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling Y. Song et al. 10.5194/hess-28-3051-2024
351. Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations W. Knoben et al. 10.5194/gmd-12-2463-2019
352. Technical note: Diagnostic efficiency – specific evaluation of model performance R. Schwemmle et al. 10.5194/hess-25-2187-2021
353. To What Extent Are Changes in Flood Magnitude Related to Changes in Precipitation Extremes? H. Do et al. 10.1029/2020GL088684
354. BULL Database – Spanish Basin attributes for Unravelling Learning in Large-sample hydrology J. Senent-Aparicio et al. 10.1038/s41597-024-03594-5
355. Hydrological Interpretation of a Statistical Measure of Basin Complexity S. Pande & M. Moayeri 10.1029/2018WR022675
356. New projections of 21st century climate and hydrology for Alaska and Hawaiʻi N. Mizukami et al. 10.1016/j.cliser.2022.100312